Course Details

Objective

On successful completion of the course you will be able to:

• To give the participants a sound understanding of the principles and the basis for applying the basic principles of modern statistical methods for analyzing the data and make the correct inferences.
• To perform data analysis and/or data investigation for what perspective.
• To use data analysis results and turns it in attractive presentations for decision making processes.

• To define the key concepts in Statistics and Sampling

• To enable the attendees to grasp the advanced information in various aspects of basic data analysis.

• To present different techniques of statistical data analysis

• To introduce the concept of regression analysis and statistical modeling to participants.

• To illustrate study cases for different applications of statistical data analysis

Content

DAILY OUTLINE

The following topics will be covered in 3 days:

INTRODUCTION & BASICS

• Introduction

• Types of Data: Measurement & Categorical Variables

• Measurement scales

• Variables

• Parameters

• Statistics:

o Descriptive Statistics

o Inferential Statistics

• Accuracy And Precision

• Summation Notation

• Confidence Intervals

• Exercises

UNIVARIATE DATA

• Central Tendency

???? Mean

???? Median

???? Mode

• Spread

???? Range

???? Semi-Interquartile Range

???? Variance

???? Standard Deviation

• Shape

???? Skew

???? Kurtosis

• Graphs

• Exercises

BIVARITE DATA

• Scatter plots

• Pearson’s Correlation

• Example Values of r

• Exercises

 

PROBABILITY

• Simple & Conditional Probability

• Probability of (A and B) and (A or B)

• Binomial Distribution

• Exercises

NORMAL DISTRIBUTION

• Definition

• Standard Normal Distribution

• Conversion to Percentiles and Back

• Exercises

SAMPLING DISTRIBUTION

• Definition

• Sampling Distribution of the Mean

• Standard Error

• Central Limit Theorem

• Difference Between Means

• Proportion

• Difference Between Proportions

• Exercises

POINT ESTIMATION

• Overview

• Characteristics of Estimators

• Estimation Variance

• Exercises

CONFIDENCE INTERVALS

• Overview

• Mean, σ Known

• Mean, σ Estimated

• General Formula

• Difference Between Means of Independence Groups: σ Known; σ Estimated

• Linear Combination of means from Independent Groups

• Exercises

LOGIC OF HYPOTHESIS TESTING

• Ruling Out Changes as an Explanation

• The Null Hypothesis

• Steps in Hypothesis Testing

• The Precise Meaning of the p Value

• At What Level is H0 Really Rejected

• Statistical and Practical Significance

• Type I and II Errors

• One- and Two-Tailed Tests (t-tests)

• Confidence Intervals and Hypothesis Testing

• Exercises

HYPOTHESIS TESTING WITH STANDARD ERRORS

• General Formula

• Tests of μ, σ Known

• Tests of μ, σ Estimated

• μ1 - μ2, Independent Groups, σ Estimated

• μ1 - μ2, Dependent Groups, σ Estimated

• Linear Combination of Means, Linear Combination of Means, Independent

   Groups

• Proportions

• Differences Between Proportions

• Exercises

POWER & *P VALUE

• Introduction

• Factors Affecting Power

o Introduction

o Size of Differences Between Means

o Significance Level

o Sample Size

o Variance

o Other Factors

• Estimating Power

• *P Value

• Exercises

 

ANALYSIS OF VARIANCE (ANOVA)

• Preliminaries

• ANOVA with 1 Between-Subjects Factor

• Tests supplementing ANOVA

• Formal Model

• Expected Mean Squares

• Exercises

PREDICTION

• Introduction

• Standard Error of the Estimate

• Partitioning the Sums of Squares

• Confidence Intervals and Significance Tests for Correlation and Regression

• Simple Linear Regression

 • Multiple Linear Regressions

• Exercises

 

TRAINING OUTCOME

• To give the participants a sound understanding of the principles and the basis for applying the basic principles of modern statistical methods for analyzing the data and make the correct inferences.
• To perform data analysis and/or data investigation for what perspective.
• To use data analysis results and turns it in attractive presentations for decision making processes.

• To define the key concepts in Statistics and Sampling

• To enable the attendees to grasp the advanced information in various aspects of basic data analysis.

• To present different techniques of statistical data analysis

• To introduce the concept of regression analysis and statistical modeling to participants.

To illustrate study cases for different applications of statistical data analysis

 

NOTE:

Pre & Post Tests will be conducted

Case Studies, Group Exercises, Group Discussions, Last Day Review & Assessments will be carried out.

 

………………………………………………………….

Methodology

A highly interactive combination of lecture and discussion sessions will be managed to maximize the amount and quality of information, knowledge and experience transfer. The sessions will start by raising the most relevant questions, and motivate everybody finding the right answers. The attendants will also be encouraged to raise more of their own questions and to share developing the right answers using their own analysis and experience.

 

All attendees receive a course manual as a reference.

 

This interactive training workshop includes the following training methodologies

30% Lectures

30% Workshops and work presentation

20% Group Work& Practical Exercises

20% Videos& General Discussions